Experimental quantum speed-up in reinforcement learning agents

As the field of artificial intelligence advances, the demand for algorithms that can learn quickly and efficiently increases. An important paradigm within artificial intelligence is reinforcement learning [1], where decision-making entities called agents interact with environments and learn by updating their behaviour based on obtained feedback. The crucial question for practical applications is how fast agents learn [2]. While various works have made use of quantum mechanics to speed up the agent’s decision-making process [3, 4], a reduction in learning time has not been demonstrated yet. Here, we present a reinforcement learning experiment where the learning process of an agent is sped up by utilizing a quantum communication channel with the environment. We further show that combining this scenario with classical communication enables the evaluation of such an improvement, and additionally allows for optimal control of the learning progress. We implement this learning protocol on a compact and fully tuneable integrated nanophotonic processor. The device interfaces with telecom-wavelength photons and features a fast active feedback mechanism, allowing us to demonstrate the agent’s systematic quantum ad-vantage in a setup that could be readily integrated within future large-scale quantum communication networks.